Deep learning using neural networks is a useful paradigm for machine learning. However, one disadvantage of deep neural networks is that it may take a long time to train and use a full machine learning model. Meanwhile, when deep neural networks are used, generally only the output of the neural network is saved. Transient information inferred by the neural network during use is generally discarded. It would be desirable to find a way to use information generated by the neural network without having to train and use a full model.